Methods and apparatus to search datasets

ABSTRACT

Methods and apparatus to search datasets are disclosed. An example disclosed method includes receiving a search request having at least two criteria and assigning the criteria to a first group according to a logical relationship between the criteria. The example method further includes determining which of the criteria in the first group is satisfied by a least amount of records in a database based on a plurality of counts, the counts respectively indicative of a number of corresponding records in the database satisfying a respective one of criteria exhibited by the database, and identifying a reduced set of records in the database to be searched, the reduced set of records corresponding to the first or second criteria that is satisfied by the least amount of records in the database, and reducing a search time associated with the search request by searching the reduced set of records from the database.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 15/824,666, filed Nov. 28, 2017, now U.S. Pat. No. 10,719,514, whichis a continuation of U.S. patent application Ser. No. 15/190,816, filedJun. 23, 2016, now U.S. Pat. No. 9,842,138, which is a continuation ofU.S. patent application Ser. No. 14/631,298, filed Feb. 25, 2015, nowU.S. Pat. No. 9,378,267, which is a continuation of U.S. patentapplication Ser. No. 13/828,780, filed Mar. 14, 2013, now U.S. Pat. No.9,002,866. U.S. patent application Ser. No. 15/824,666, U.S. patentapplication Ser. No. 15/190,816, U.S. patent application Ser. No.14/631,298 and U.S. patent application Ser. No. 13/828,780 are herebyincorporated herein by reference in their entireties. Priority to U.S.patent application Ser. No. 15/842,666, U.S. patent application Ser. No.15/190,816, U.S. patent application Ser. No. 14/631,298, and U.S. patentapplication Ser. No. 13/828,780, is claimed.

FIELD OF THE DISCLOSURE

This patent relates generally to data management and, more particularly,to methods and apparatus to search datasets.

BACKGROUND

Many entities such as retail establishments and product manufacturersare interested in the shopping activities, behaviors, and/or habits ofconsumers. Consumer activity related to shopping can be used tocorrelate product sales with particular shopping behaviors and/or toimprove timing or placement of product offerings, product promotions,and/or advertisements. To make use of such information, market analysisentities typically utilize a plurality of statistical tools to study,evaluate, and/or predict market conditions and/or consumer behavior. Toimplement some such statistical tools, data related to products on themarket is stored in searchable databases and/or data structures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example search provider.

FIG. 2 is a block diagram of an example implementation of the examplesearcher of FIG. 1.

FIG. 3 is an illustration of first example information generated by theexample searcher of FIGS. 1 and/or 2.

FIG. 4 is an illustration of second example information generated by theexample searcher of FIGS. 1 and/or 2.

FIGS. 5 and 6 are flowcharts representative of example machine readableinstructions that may be executed to implement the example searcher ofFIGS. 1 and/or 2.

FIG. 7 is a block diagram of an example processing system capable ofexecuting the example machine readable instructions of FIGS. 5 and/or 6to implement the example searcher of FIGS. 1 and/or 2.

DETAILED DESCRIPTION

Search providers typically manage large datasets including recordsrelated to one or more types of information. Some datasets related toproduct information include records having one more characteristicsand/or one or more potential values for the characteristics. Forexample, a product database may include a first record corresponding toa first product (e.g., a jacket) having characteristics of color, size,style, and brand. The product database may also include a second recordcorresponding to a second product (e.g., a soft drink) havingcharacteristics of volume, flavor, and brand. Further, each of thecharacteristics has a plurality of potential values. For example, thecolor characteristic of the first record has a certain number ofpotential values, such as red, blue, yellow, etc. Further, brandcharacteristic of the second record has a certain number of potentialvalues, such as cherry, orange, lemon lime, etc. Each characteristic ofa record and the corresponding value is referred to herein as acharacteristic-value pair. For example, a first characteristic-valuepair of a record corresponding to a soft drink is ‘Volume=100 mL.’ Asecond characteristic-value pair for the soft drink may be‘Brand=Pepsi®.’ While the characteristic-value pairs described hereinare related to consumer goods, example methods, apparatus, and/orarticles of manufacture disclosed herein can be implemented inconnection with additional or alternative types of records of additionalor alternative types of datasets.

The size of datasets including the characteristic-value pairs canincrease quickly (e.g., exponentially) when products are added to (e.g.,released into) the marketplace and, therefore, added to the productdatabase. The increase in size and complexity of the datasets presentschallenges to the corresponding search providers. For example, searchproviders associated with burdensome (e.g., large and complex) datasetsare challenged to deliver search results in a timely manner. Given thesize and complexity of the datasets, processing of search results iscomputationally expensive and often takes a significant amount of time.However, any increase in the time taken to deliver search results may bedetrimental to the search providers, as search requesters typicallyplace a high importance on speed of delivery for search results. Thatis, given a choice among multiple search providers, search requestersare more likely to choose a search provider that delivers resultsquickly than one that delivers results slowly. Therefore, searchproviders benefit from an ability to search and convey search results athigher speeds.

Example methods, apparatus, systems, and/or articles of manufacturedisclosed herein improve search speeds and, thus, enable searchproviders to deliver search results faster than previous systems. Asdescribed in greater detail below, examples disclosed herein generateone or more count statistics regarding one or more aspects of therecords of a dataset to facilitate a reduction in an amount of recordsto be searched in response to a search request. In particular, examplesdisclosed herein calculate a count of records or entries in the datasetfor some or all of the individual characteristic-value pairs present inthe dataset. In other words, examples disclosed herein calculate a firstcount equal to an amount of records currently present in the datasetthat include a first particular characteristic-value pair (e.g.,Brand=Pepsi®), a second count equal to an amount of records currentlypresent in the dataset that include a second particularcharacteristic-value pair (e.g., Flavor=Cherry), and so on for some orall of the characteristic-value pairs occurring in the dataset. Examplesdisclosed herein store the calculated counts for the records of thedataset to form a collection of count statistics. While the examplecounts disclosed herein are described in connection withcharacteristic-value pairs, examples disclosed herein can be implementedin connection with additional or alternative types of search criteria.For example, the count statistics generated by examples disclosed hereinmay be indicative of numbers of records including respective keywords.

Example methods, apparatus, and/or articles of manufacture disclosedherein utilize the count statistics to service search requests fasterthan previous systems. As described in greater detail below, in responseto a search request including a plurality of search criteria (e.g.,characteristic-value pairs), examples disclosed herein reference thepreviously generated count statistics to develop a reduced set (e.g.,subset) of records on which at least one component of the requestedsearch is performed. That is, rather than searching the entire databaseof product records for each of the search criteria of a search request,examples disclosed herein utilize the previously generated countstatistics to lessen the number of records that need to be searched inconnection with one or more of the search criteria of the requestedsearch.

To generate the reduced set of records, examples disclosed hereinidentify a driving search criterion (e.g., characteristic-value pair)for logically related groups of search criteria present in the requestedsearch. When a search request includes a group of criteria related toeach other via one or more logical operators (e.g., AND, OR, etc.) inthe search request definition, the group of terms is referred to hereinas a logically related group of search criteria. Search providerstypically enable requesters to include logical operators (e.g., AND, OR,etc.) in search requests to define searches beyond the individualcriteria (e.g., characteristic-value pairs, keywords, etc.) of thesearch request such that more relevant search results are delivered tothe requester.

Examples disclosed herein recognize that a driving search criterion canbe identified for such groups of search criteria by generating andanalyzing the count statistics disclosed herein. For example, for asearch request including first and second search criteria logicallyrelated via an AND operator, examples disclosed herein reference thecount statistics to determine which one of the first and second searchcriteria is associated with the least amount of records in the database.In other words, examples disclosed herein utilize the previouslygenerated count statistics to determine a first amount of recordsmeeting the first search criteria and a second amount of records meetingthe second search criteria. Examples disclosed herein select the lesserof the first and second amounts as the driving search criterion for thelogically related group of search criteria. Examples disclosed hereinidentify the search criterion met in the lesser amount of records as thedriving search criterion because any records deemed to have met thesearch request as a whole (e.g., the logical definition of the searchrequest, including the AND operator) must at least meet the drivingsearch criterion. Put another way, the driving search criterionidentified by examples disclosed herein represents the criterion of thecorresponding search that is met by the least number of records in thedatabase and, thus, defines the maximum number of records of thedatabase that can meet the entire search definition.

Accordingly, examples disclosed herein develop a reduced set of records(e.g., a subset) that includes records of the database meeting theselected driving search criterion. In other words, example disclosedherein use the selected driving search criterion as a basis to develop asubset of the records of a database for searching with the non-drivingcriteria. To continue the above example, assume that the first searchcriterion is selected as the driving search criterion for the logicallyrelated (e.g., via an AND operator) first and second search criteria. Insuch instances, examples disclosed herein define (e.g., by setting aflag or indicator in association with individual ones of the records)the reduced set of records to include records meeting the first searchcriterion. Examples disclosed herein then perform a search on thereduced set of records using one or more second search criterion. Thatis, examples disclosed herein determine which one(s) of the reduced setof records satisfies the one or more second search criterion. Thus,rather than having to determine which records of the entire databasemeet the one or more second search criterion, examples disclosed hereinsearch for the second criterion on only a subset of the databaserecords. Some databases (e.g., product tracking databases for consumergoods) include large amounts (e.g., billions, trillions, etc.) ofrecords and/or characteristic-value pair possibilities. Thus, as furtherdemonstrated below, the reduction in the amount of records to searchprovided by examples disclosed herein dramatically reduces search times.

FIG. 1 is a block diagram of an example system in which example methods,apparatus, and/or articles of manufacture disclosed herein may beimplemented. The example of FIG. 1 includes a search provider 100 thatreceives search requests from search requesters, one of which is shownin FIG. 1 with reference numeral 102. In some examples, the searchprovider 100 is a proprietor of a search engine (e.g., Nielsen®,Google®) and the search requester 102 communicates with the searchprovider 100 via publically available communication systems (e.g., theInternet). Additionally or alternatively, the example search requester102 communicates with the search provider 100 according to an agreement(e.g., contract) such that the search provider 100 provides one or moresearch services to the search requester 102 (e.g., via publicallyavailable and/or private communication systems).

In the illustrated example of FIG. 1, the search provider 100 providesmarket research services to the search requester 102 including access toa product database 104 managed by the search provider 100. The exampleproduct database 104 is a collection of records related to products(e.g., consumer goods) available in one or more marketplaces (e.g.,geographic areas, industries, virtual marketplaces, markets, etc.). Inthe example of FIG. 1, the search provider 100 strives to store a recordin the product database 104 for each product available in the one ormore marketplaces tracked by the search provider 100. The individualrecords of the example product database 104 of FIG. 1 have one or morecharacteristics and corresponding values for the characteristics,thereby forming a plurality of characteristic-value pairs. As describedabove, each characteristic of a particular record has one or morepotential values. That is, different versions of a product have acorresponding record in the product database 104 depending on, forexample, variation(s) in the different values for the characteristic(s)of the products. Thus, the example product database 104 of FIG. 1reflects the available products in different marketplaces and thedifferent aspects associated with the available products. In suchinstances, the search requester 102 is, for example, a person or entityinterested in product(s) of marketplace(s), such as a media planner, anadvertiser, a marketing entity, a product developer, a manufacturer ofconsumer goods, a retailer, etc.

The example product database 104 of FIG. 1 is maintained by coders(e.g., programmers, administrators, data entry professionals, etc.)associated with the search provider 100, one of which is shown in FIG. 1with reference numeral 106. When the search provider 100 becomes awareof an available product not having a corresponding record in the productdatabase 104, the example coder 106 of FIG. 1 is tasked with creating anew record for the product and integrating the new record into theexample product database 104. In some examples, the coder 106 is alsotasked with determining whether a particular product is actually newwith respect to the product database 104. In other words, a person ordevice associated with the search provider 100 may have reason tobelieve that a particular product may not have a corresponding record inthe product database 104 (e.g., the person or device is not familiarwith a product and wants to ensure that the product database 104includes a corresponding record). Additionally or alternatively, aperson or device associated with the search provider 100 may provideinformation regarding particular product(s) to the example searchprovider 100 according to a schedule (e.g., in a weekly batch) and thesearch provider 100 may process the received information to determinewhether the product database 104 includes record(s) corresponding to theproduct(s).

In the example of FIG. 1, instances of product data 108, 110, 112associated with products of interest (e.g., products potentially notrecorded in the product database 104) are received by the coder 106 inconnection with, for example, a request to create a new record for thecorresponding product in the product database 104 and/or a request todetermine whether the product database 104 already includes a record forthe corresponding product. In such instances, the coder 106 determineswhether the example product database 104 includes a record correspondingto the product data 108-112. If the example product database 104 doesnot include a record for any of the product data 108-112, the examplecoder 106 of FIG. 1 creates a record for the new product(s).

Each of the example instances of product data 108-112 of FIG. 1 includesa description of the corresponding product such as, for example, aspecification and/or lists of features. To determine whether the exampleproduct database 104 of FIG. 1 includes a record associated with, forexample, the first instance of product data 108 and/or to lookup aparticular product in the product database 104, the example coder 106submits a search request to an example searcher 114 constructed inaccordance with example methods, apparatus, and/or articles ofmanufacture disclosed herein. The search request submitted by theexample coder 106 is developed or written by the coder 106 based ondescriptive information of the first instance of product data 108 andincludes, for example, one or more characteristic-value pairs. Forexample, when the first instance of product data 108 corresponds to asoft drink and the corresponding descriptive information indicates thatthe soft drink is a Pepsi® product and has a volume of 100 mL, the coder106 generates a search request including a first characteristic-valuepair of ‘Brand=Pepsi®’ and a second characteristic-value pair of‘Volume=100 mL.’ In some instances, the characteristic-value pair(s) ofthe search request are logically related via, for example, a logicaloperator used by the example coder 106 to further define the searchrequest. The example coder 106 expects search results indicative ofwhether the example product database 104 includes a record correspondingto such a soft drink.

In the illustrated example of FIG. 1, the example search provider 100receives similar search requests from, for example, the search requester102 of FIG. 1. That is, the example search requester 102 submits searchrequest(s) including one or more characteristic-value pairs, which mayor may not be logically related by a logical operator, to the examplesearcher 114 of FIG. 1. For example, when the search requester 102 is amedia planner and/or advertiser interested in products of a particularbrand, the example search requester 102 submits a search request to theexample searcher 114 that includes a characteristic-value pairidentifying the brand of interest. Additional or alternative types ofsearch requests, requesters, types of search criteria, etc. arepossible.

As described in detail below in connection with FIGS. 2-5, the examplesearcher 114 of FIG. 1 generates count statistics for the exampleproduct database 104, uses the count statistics to develop a reduced setof records to search in response to a search request, and searches thereduced set of records to deliver search results to a device associatedwith the search request (e.g., a computing device associated with thecoder 106 and/or a computing device associated with the search requester102) in a timely manner. In some instances, the example coder 106 ofFIG. 1 utilizes the search results provided by the example searcher 114to maintain the product database 104. To continue the above example, ifthe searcher 114 indicates that the product database 104 does notcurrently include a record associated with the first instance of productdata 108, the example coder 106 generates a new record for thecorresponding product and stores the new record in the example productdatabase 104. Alternatively, if the product database 104 alreadyincludes a record associated with the first instance of product data108, the example searcher 114 informs the coder 106 that the productdatabase 104 includes a corresponding product record and that adding anew record for the product data 108 is not necessary.

As the example product database 104 of FIG. 1 includes a large number ofrecords, most of which include a plurality of aspects orcharacteristics, delivering search results in a timely manner is achallenge. In this context, the benefits provided by the examplesearcher 114 are significant. In particular, the example searcher 114 ofFIG. 1 reduces search delivery time and conserves computationalresources by reducing the number of records accessed during searches.Further, users (e.g., the example coder 106 and/or the search requester102) may utilize the example searcher 114 of FIG. 1 frequently and,thus, repeatedly receive the benefit of faster delivery of searchresults provided by the example searcher 114.

FIG. 2 is a block diagram of an example implementation of the examplesearcher 114 of FIG. 1. FIG. 2 is described herein in conjunction withFIGS. 3 and 4, which are illustrations of example information utilizedby the example searcher 114 of FIGS. 1 and/or 2 and informationgenerated by the example searcher 114 of FIGS. 1 and/or 2. Theinformation presented in FIGS. 3 and 4 is for purposes of illustrationand has been scaled down to amounts of data smaller than a typicalinstance of the product database 104 of FIG. 1 for purposes of brevityand enhanced clarity. While example information from FIGS. 3 and 4 isreferred to herein in connection with FIG. 2, the example searcher 114of FIG. 2 can interact with and generate additional type(s) and/oramount(s) of information.

The example searcher 114 of FIG. 2 includes a statistics generator 200to generate count statistics based on the information in the exampleproduct database 104 of FIG. 1. To begin generation of the countstatistics, the example statistics generator 200 of FIG. 2 identifiesthe different characteristic-value pairs present in the product database104. In some examples, the statistics generator 200 identifiescharacteristic-value pairs associated with active products (e.g.,currently being made for sale and/or marketing, as indicated by acorresponding active universal product code (UPC)) in the exampleproduct database 104. In some examples, the product database 104 and/orthe statistics generator 200 maintains a list of characteristic-valuepairs currently stored in the product database 104. In such instances,the example statistics generator 200 references the list to identify thecurrent characteristic-value pairs of the product database 104.

For each of the identified characteristic-value pairs of the productdatabase 104, the example statistics generator 200 of FIG. 2 maintains acount in a count statistics database 202. The example statisticsgenerator 200 of FIG. 2 generates and/or updates the count statisticsdatabase 202 according to a schedule (e.g., nightly, weekly, etc.)and/or in response to addition(s) to the example product database 104 ofFIG. 1. In some examples, the statistics generator 200 determines thatone or more new products have been entered into the product database 104since the previous update of the count statistics database 202. In suchinstances, the example statistics generator 200 analyzes data associatedwith the new product(s) and updates (e.g., increments, decrements,and/or otherwise increases or decreases) the corresponding counts in theexample count statistics database 202 accordingly. In some examples, thestatistics generator 200 of FIG. 2 generates and/or updates thestatistics during a period of expected low activity (e.g., inactivity,such as at night and/or a weekend day). Additional or alternative updatescheme(s), timing, and/or technique(s) are possible.

FIG. 3 includes example counts 300 of the example count statisticsdatabase 202 of FIG. 2 after a generation and/or update of the countstatistics performed by the example statistic generator 200 of FIG. 2.In the illustrated example of FIG. 3, the product database 104 includesfive (5) characteristic-value pairs for which counts are to begenerated. As mentioned above, the example data of FIG. 3 is scaled downfrom the typical volume of a product database for purposes ofillustration and enhanced clarity. A typical instance of the exampleproduct database 104 of FIGS. 1 and/or 2 is likely to includesignificantly more than five (5) characteristic-value pairs. In theexample of FIG. 3, the example statistics generator 200 has determinedthat the product database 104 includes fifty (50) records having thecharacteristic-value pair ‘Color=Blue,’ thirty (30) records having thecharacteristic-value pair ‘Color=Red,’ five (5) records having thecharacteristic-value pair ‘Volume=100 mL,’ fifteen (15) records havingthe characteristic-value pair ‘Volume=250 mL,’ and one hundred (100)records having the characteristic-value pair ‘Brand=Pepsi®.’ Thecharacteristic-value pairs shown in the example counts 300 of FIG. 3 arereferred to herein as static characteristic-value pairs in that therespective static characteristic-value pairs satisfy a single, fixedsearch criterion. That is, the static characteristic-value pair‘Color=Blue’ satisfies only one corresponding search criterion (e.g.,Color/Blue). However, other characteristic-value pairs satisfy more thanone search criterion when the corresponding search criterion to besubmitted by a search requester can include, for example, a range.Characteristic-value pairs that satisfy more than one search criterionand that can be searched using a range are referred to herein as dynamiccharacteristic-value pairs. An example dynamic characteristic-value pairis a date of creation for a product record. The date of creation for arecord is dynamic in the sense that the date of creation may be equalto, greater than, or less than a particular search criterion. That is, asearch request can include a search criterion of, for example,‘CreationDate/>=12-NOV-2009,’ which corresponds to a request to retrieveproduct records having a date of creation greater than or equal to Nov.12, 2009. Thus, dynamic characteristic-value pairs correspond to aspectsof the product records that are searchable using a range of values.

In the illustrated example of FIG. 2, the example statistics generator200 of FIG. 2 generates counts for static characteristic-value pairsaccording to, for example, a schedule and/or in response to new productrecords being added to the product database 104. Thus, in theillustrated example, the statistics generator 200 regularly maintains acount for each static characteristic-value pair of the product database104. For example, the example statistics generator 200 of FIG. 2calculates how many records of the product database satisfy thecharacteristic-value pair ‘Color=Blue’ each time an update of thestatistics generator 200 is triggered (e.g., according to a schedule orin response to an addition of record(s) to the product database 104). Incontrast, for a dynamic characteristic-value pair of the productdatabase 104, the example statistics generator 200 of FIG. 2 generates acount in response to receiving a corresponding search criterion inconnection with a received search request. That is, the examplestatistics generator 200 of FIG. 2 waits to receive a search criterionassociated with the respective dynamic characteristic-value pairs of theproduct database 104 before generating a corresponding count. Theutilization of the count statistics generated by the example statisticsgenerator 200 and stored in the example count statistics database 202are described in greater detail below.

As described above in connection with FIG. 1, the example searcher 114receives a search request (e.g., from the coder 106, the searchrequester 102, or other user(s)) that includes one or more searchcriteria. The search criteria received by the example searcher 114 ofFIG. 2 includes, for example, one or more characteristic-value pairs(e.g., static characteristic-value pair(s) and/or dynamiccharacteristic-value pair(s)) indicative of one or more productcharacteristics (e.g., brand, color, volume, etc.) and/or aspects of thecorresponding product records. For example, in response to a taskassigned to the coder 106 of FIG. 1 related to the first product data108 (FIG. 1), the example coder 106 generates a search request includingone or more characteristic-value pairs indicative of one or more aspectsof the first product data 108 for submission to the searcher 114. Insuch instances, the example coder 106 submits the generated searchrequest to the example searcher 114 to determine whether the productdatabase 104 (FIG. 1) already includes a product corresponding to thefirst product data 108 and/or to identify similar products havingrecords in the product database 104. Additionally or alternatively, theexample search requester 102 may want to identify products available ina marketplace having one or more particular aspects, such as aparticular brand, a particular color, a particular quantity, and/or anyother suitable aspect(s). In such instances, the search requester 102generates a search request including one or more characteristic-valuepairs corresponding to the aspects of interest and submits the generatedsearch request to the example search request to the example searcher 114of FIG. 2. For purposes of illustration and brevity, the example of FIG.2 shows the searcher 114 receiving a search request from the examplecoder 106 of FIG. 1.

In response to receiving the search request from the coder 106, aninterface 204 of the example searcher 114 of FIG. 2 determines whetherthe search request includes a single search criterion or multiple searchcriteria. When the example interface 204 of FIG. 2 determines that thesearcher 114 of FIG. 2 has received a search request including a singlesearch criterion (e.g., a single characteristic-value pair), the exampleinterface 204 provides the single search criterion to an analyzer 206 ofthe example searcher 114. In the illustrated example of FIG. 2, theanalyzer 206 searches the product database 104 to identify record(s) ofthe product database 104 meeting the single search criterion. Theexample analyzer 206 of FIG. 2 implements any suitable search mechanismand/or technique to parse through the records of the product database104 using the search criterion. For example, when the single searchcriterion of the search request is a characteristic-value pair, theexample analyzer 206 of FIG. 2 compares the characteristic-value pairsof the product database 104 with the characteristic-value pair of thesingle-criterion search request.

If the example record 206 of FIG. 2 determines that the product database104 includes product record(s) that match the product data of the searchrequest, the example analyzer 206 provides the matching record(s) and/oridentifier(s) associated with the matching record(s) to the interface204. The example interface 204 of FIG. 2 communicates the matchingrecord(s) and/or the corresponding identifier(s) to one or more devicesassociated with the search request. In the illustrated example of FIG.2, the interface 204 provides the matching record(s) and/or thecorresponding identifier(s) to a network address associated with thereceived search request, which corresponds to a computing device of thecoder 106. Additionally or alternatively, the example interface 204 ofFIG. 2 can provide access to the matching record(s) by, for example,communicating a link (e.g., hyperlink, path name, etc.) and/orinstructions on how to access the matching record(s) to the coder 106.

While some search requests include a single search criterion, suchsearches are likely to generate a large number of search results. Toobtain more focused search results, search requesters often submitsearch requests that include multiple search criteria. For example, asearch request submitted by the coder 106 of FIG. 1 may include a firstcharacteristic-value pair (e.g., Brand=Pepsi®), a secondcharacteristic-value pair (e.g., Color=Red), a thirdcharacteristic-value pair (e.g., Volume=100 mL), and so on. In suchinstances, the multiple search criteria are logically related via one ormore logical operators, such as an OR operator or an AND operator. Theexample searcher 114 of FIG. 2 includes a grouper 208 to identifylogical relationship(s) between search criteria and to form group(s) ofsearch criteria according to the logical relationship(s). In someexamples, the search requester provides the logical operators as part ofthe search request. In such instances, the example grouper 208 uses thelogical operators provided by the search requester to form one or moregroups of search criteria reflecting the logical relationship(s)established by the search requester. In some examples, the searchrequester does not provide an explicit logical operator between some orany of the search criteria. In such instances, the example grouper 208of FIG. 2 treats the individual search criteria as related via an ORoperator. Alternatively, when the search requester does not include anexplicit logical operator between search criteria, the example grouper208 of some examples treats individual search criteria as related via anAND operator. The example grouper 208 of FIG. 2 is configurable to alterone or more setting related to the treatment of search criteria notexplicitly tied together via a logical operator.

When the example grouper 208 of FIG. 2 identifies search criteriarelated via an AND operator, the example grouper 208 groups theAND-related search criteria by, for example, assigning a shared groupidentifier to the search criteria and/or placing the AND-related searchcriteria in a shared portion of a data structure. The example of FIG. 3includes an example grouping definition 302 corresponding to a searchcriteria group 304 generated by the example grouper 208 of FIG. 2. Theexample grouping definition 302 corresponds to a search requestsubmitted to the searcher 114 of FIG. 2 by the coder 106 including afirst search criterion ‘Brand=Pepsi®’ and a second search criterion‘Volume=100 mL’ logically related via an AND operator. The coder 106submits such a search request in response to, for example, determiningthat the first product data 108 of FIG. 1 corresponds to a Pepsi® softdrink made available in a 100 mL container. In response to receivingsuch a search request, the example grouper 208 generates the groupingdefinition 302 of FIG. 3 indicative of the desire of the coder 106 toretrieve records having the characteristic-value pair ‘Brand=Pepsi®’ andthe characteristic-value pair ‘Volume=100 mL.’ Because the first andsecond search criteria are logically related by an AND operator, thegrouping definition 302 includes the search criteria group 304 includingboth the first and second search criteria.

In addition to providing search requests having multiple search criteriato the example grouper 208, the example interface 204 of FIG. 2 providesthe search criteria of multiple-criteria search requests to a statisticsobtainer 210. The example statistics obtainer 210 of FIG. 2 uses thereceived search criteria to reference the example count statisticsdatabase 202 of FIG. 2. In particular, the example statistics obtainer210 of FIG. 2 retrieves the individual counts associated with each ofthe received search criteria. That is, the example statistics obtainer210 of FIG. 2 determines, via the count statistics database 202, howmany records of the product database 104 satisfy a first searchcriterion of a multiple-criteria search request, how many records of theproduct database 104 satisfy a second search criterion of themultiple-criteria search request, how many records of the productdatabase 104 satisfy a third search criterion of the multiple-criteriasearch request, etc. FIG. 3 includes an illustration of the counts 306retrieved by the example statistic obtainer 210 in connection with theabove example search request including the first search criterion‘Brand=Pepsi®’ and the second search criterion ‘Volume=100 mL.’ In theillustrated example of FIG. 3, the statistics obtainer 210 hasdetermined that the product database 104 includes one hundred (100)records satisfying the first search criterion ‘Brand=Pepsi®’ and five(5) records satisfying the second search criterion ‘Volume=100 mL.’

The example searcher 114 of FIG. 2 includes a driving criterionidentifier 212 that receives the counts retrieved by the examplestatistics obtainer 210 of FIG. 2. The example driving criterionidentifier 212 of FIG. 2 selects one of the search criteria of themultiple-search criteria group 304 as the driving search criterion forthe search request. In the illustrated example, the driving criterionidentifier 212 identifies the lowest of the received counts as thedriving criterion. Thus, the example driving criterion identifier 212selects the search criterion of the search request that satisfies theleast number of records of the product database 104. As described above,identification of the driving criterion enables the example searcher 114to avoid having to search the entire product database 104 for one ormore search criteria of a received search request, thereby reducing theamount of records to search and increasing search speeds. That is, theselected driving search criterion is used as a basis to generate areduced set of records to be searched rather than the entire productdatabase 104. In the illustrated example of FIG. 3, the drivingcriterion identifier 212 compares the count of one-hundred (100) recordsassociated with the first search criterion ‘Brand=Pepsi®’ to the countof five (5) records associated with the second search criterion‘Volume=100 mL.’ As five (5) is less than one hundred (100), the exampledriving criterion identifiers 212 of FIG. 2 selects the second searchcriterion ‘Volume=100 mL’ as the driving criterion for the examplesearch request of FIG. 3.

In the illustrated example of FIG. 2, the driving criterion identifier212 provides a reduced set generator 214 with an indicator or identifierindicative of which search criterion is to be used as the driving searchcriterion for the corresponding group(s) of the search request. Further,the example grouper 208 provides the reduced set generator 214 with thesearch criteria as grouped according to the logical operators of thesearch request. Thus, the example reduced set generator 214 receivesgroup(s) of search criteria from the example grouper 208 and anindication for each one of the group(s) from the driving criterionidentifier 212 of which criterion has been selected as the drivingcriterion for the respective group. The example reduced set generator214 of FIG. 2 uses the driving criterion as a basis for generating areduced set of records corresponding to a subset of the records of theproduct database 104. In particular, for each of the receivedAND-related groups of search criteria (e.g., each set of terms joined byan AND logical operator) generated by the grouper 208, the examplereduced set generator 214 of FIG. 2 queries the product database 104with the corresponding driving criterion to retrieve the records of theproduct 104 satisfying the driving criterion.

For example, if a first driving search criterion selected for a firstgroup of a search request corresponds to a first characteristic-valuepair that is present in one thousand (1,000) records of the productdatabase 104, the example reduced set generator 214 of FIG. 2 retrievesthe one thousand (1,000) records of the product database 104 satisfyingthe first driving search criterion as the reduced set of records. Insome examples, the search request includes a second group for which asecond driving search criterion is selected. For example, the seconddriving search criterion may correspond to a second characteristic-valuepair that is present in two thousand (2,000) records of the productdatabase 104 satisfying the second driving search criterion. In suchinstances, the example reduced set generator 214 generates a firstreduced set of one thousand (1,000) records for the first AND-relatedgroup of the search request and a second reduced set of two-thousand(2,000) records for the second AND-related group of the search request.

FIG. 3 includes an example reduced set of records 308 generated by theexample reduced set generator 214 of FIG. 2. As described above, theexample reduced set of records 308 of FIG. 3 includes the records of theproduct database 104 that satisfy the driving search criterion of theexample group 304 formed by the example grouper 208. As the drivingsearch criterion of the example of FIG. 3 is ‘Volume=100 mL,’ therecords of the example reduced set of records 308 are the five (5)records of the product database 104 that include thecharacteristic-value pair ‘Volume=100 mL.’ In comparison, the productdatabase 104 includes a total of two hundred (200) records. Thus, theexample searcher 114 of FIG. 2 enables a search for ‘Brand=Pepsi®’ inthe reduced set of records 308 that includes five (5) records, asopposed to having to search the two hundred (200) records of the productdatabase 104 for ‘Brand=Pepsi®.’ As a result, the time required for thesearch and the burden on the processing capabilities of the system arereduced.

In the illustrated example of FIG. 2, the results of the reduced setgenerator 214 (e.g., the reduced set of records 308 of FIG. 3) arestored as a reduced set of records 216 accessible by the exampleanalyzer 206 of FIG. 2. As described above, the example analyzer 206analyzes a set of records to identify record(s) of the set that satisfyspecified criteria. The analysis of the product database 104 performedby the example analyzer 206 of FIG. 2 in connection with single-criteriasearch requests is described above. While the example analyzer 206 ofFIG. 2 searches the product database 104 for single-criteria searchrequests, the example analyzer 206 searches the reduced set of records216 in connection with multiple-criteria search requests. In particular,the example analyzer 206 of FIG. 2 determines which of the reduced setof records 216 satisfies the criterion or criteria of the correspondinggroup (e.g., as generated by the group generator 208) other than thedriving search criterion (e.g., as identified by the driving criterionidentifier 212). Without the example reduced set of records 216 providedby the example searcher 114 of FIG. 2, the analyzer 206 would need toperform a search of the entire product database 104 for each one of thesearch criteria of the corresponding search request. In contrast, bygenerating the example reduced set of records 216, the example searcher114 of FIG. 2 enables the example analyzer 206 to search a subset of theproduct database 104 for one or more of the search criteria. Forexample, when the searcher 114 is processing the example search requestof FIG. 3, the analyzer 206 determines which of the reduced set ofrecords 308 includes the non-driving search criterion “Brand=Pepsi®.’ Todo so, the example analyzer 206 of FIG. 2 is tasked with analyzing five(5) records instead of having to search the two hundred (200) records ofthe product database 104. As the illustrated examples are scaled downrepresentations of typical product databases, which can include millionsof records, the benefits in search speeds (e.g., via the reduction inthe amount of records to be searched) are likely greater than theillustrated examples. In the example of FIG. 3, the analyzer 206identifies two (2) of the records of the reduced set of records 308 assatisfying the non-driving criterion ‘Brand=Pepsi®.’ A collection of thetwo (2) records meeting the example search request of FIG. 3 is storedas search results 310 in the example of FIG. 3.

In the illustrated example of FIG. 2, the example analyzer 206 providesthe search results to the example interface 204, which provides thesearch results and/or access to the search results to the coder 106.Thus, the example searcher 114 of FIG. 2 provides the coder 106 withsearch results without having to search the entire product database 104with each of the search criteria (when the search request includesmultiple search criteria). Accordingly, the example searcher 114 of FIG.2 significantly decreases the amount of time and the amount of computerprocessing resources needed to generate and deliver search results.

FIG. 4 depicts another example instance of the example product database104 and another example search request serviced by the example searcher114 of FIG. 2. In the illustrated example of FIG. 4, the productdatabase 104 includes a set of records having the statistics 400generated by the example statistics generator 200 of FIG. 2. The examplestatistics 400 shown in FIG. 4 are stored in the count statisticsdatabase 202 of FIG. 2. The criteria of the example search request ofFIG. 4 is received by the example grouper 208 of FIG. 2 (e.g., via theinterface 204), which identifies a logical relationship for each of thesearch criteria. In the example of FIG. 4, the grouper 208 generates acriteria grouping 402 defined to include a first criteria group 404 anda second criteria group 406. The first example group 404 of FIG. 4includes the search criteria ‘Color=Red’ and ‘Volume=100 mL.’ The secondexample group 406 of FIG. 4 includes the search criteria ‘Color=Blue’and ‘Volume=250 mL.’

The example statistics obtainer 210 of FIG. 2 obtains the countstatistics 408 of FIG. 4 for each of the search criteria of the receivedsearch request. In the illustrated example, the statistics obtainer 210obtains the statistics for the static characteristic-value pairs(‘Brand=Pepsi®,’ ‘Color=Red,’ ‘Volume=100 mL,’ and ‘Color=Blue,’‘Volume=250 mL’) from the count statistics database 202 of FIG. 2.Further, the example statistics generator 200 responds to the examplesearch request of FIG. 4 by generating a count for the dynamiccharacteristic-value pair ‘RecordCreatedDate>=12-Nov-2009.’

The example driving criterion identifier 212 of FIG. 2 uses the countstatistics 408 to select one of the search criteria for each group asthe driving search criterion for the respective group. In theillustrated example, the driving criterion identifier 212 selects thecharacteristic-value pair ‘Color=Red’ for the first group 404 becauseless records of the product database 104 satisfy thecharacteristic-value pair ‘Color=Red’ than the characteristic-value pair‘Volume=100 mL.’ Further, the driving criterion identifier 212 selectsthe characteristic-value pair ‘Volume=250 mL’ for the second group 406because less records of the product database 104 satisfy thecharacteristic-value pair ‘Color=Blue.’

Using the respective driving criteria, the example reduced set generator214 of FIG. 2 generates reduced sets of records for each of the groupsidentified by the example grouper 208. The example of FIG. 4 includes afirst reduced set of records 412 including records of the productdatabase 104 that satisfy the first driving criterion ‘Color=Red’ and asecond reduced set of records 414 including records of the productdatabase 104 that satisfy the second driving criterion ‘Volume=250 mL.’In the illustrated example of FIG. 4 the analyzer 206 searches the firstreduced set of records 412 for the non-driving criterion ‘Volume=100 mL’and the second reduced set of records 414 for the non-driving criterion‘Color=Blue.’ Instead of having to search the entire product database104 for the non-driving criterion, the example searcher 114 of FIG. 2enables the analyzer 206 to search twenty (20) records for thenon-driving criterion ‘Volume=100 mL’ and forty (40) records for thenon-driving criterion ‘Color=Blue.’

The example analyzer 206 of FIG. 2 combines the results of the performedsearches to form a set of search results 416 corresponding to therecords of the product database 104 that meet the criteria of the firstgroup 404 OR the criteria of the second group 406. In the illustratedexample, the analyzer 206 of FIG. 2 performs additional searching on thegenerated search results 416 for the characteristic-value pair‘Brand=Pepsi®’ and the characteristic-value pair‘RecordCreatedDate>=12-Nov-2009.’ The results of the additionalsearching represent the search results to be conveyed to the searchrequester (e.g., the example coder 106 of FIG. 1).

While an example manner of implementing the searcher 114 of FIG. 1 hasbeen illustrated in FIG. 2, one or more of the elements, processesand/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example statistics generator 200, the example interface204, the example analyzer 206, the example grouper 208, the examplestatistics obtainer 210, the example driving criterion identifier 212,the example reduced set generator and/or, more generally, the examplesearcher 114 of FIG. 2 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example statistics generator 200, theexample interface 204, the example analyzer 206, the example grouper208, the example statistics obtainer 210, the example driving criterionidentifier 212, the example reduced set generator and/or, moregenerally, the example searcher 114 of FIG. 2 could be implemented byone or more circuit(s), programmable processor(s), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)), etc. When any ofthe system or apparatus claims of this patent are read to cover a purelysoftware and/or firmware implementation, at least one of the examplestatistics generator 200, the example interface 204, the exampleanalyzer 206, the example grouper 208, the example statistics obtainer210, the example driving criterion identifier 212, the example reducedset generator and/or, more generally, the example searcher 114 of FIG. 2are hereby expressly defined to include a tangible computer readablestorage medium such as a storage device (e.g., memory) or an opticalstorage disc (e.g., a DVD, a CD, a Bluray disc) storing the softwareand/or firmware. Further still, the example searcher 114 of FIG. 2 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIG. 2, and/or may include more thanone of any or all of the illustrated elements, processes and devices.

FIGS. 5 and 6 are flowcharts representative of example machine readableinstructions for implementing the example searcher of FIGS. 1 and/or 2.In the example flowcharts of FIGS. 5 and 6, the machine readableinstructions comprise program(s) for execution by a processor such asthe processor 712 shown in the example processing platform 700 discussedbelow in connection with FIG. 7. The program(s) may be embodied insoftware stored on a tangible computer readable storage medium such as aCD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), aBlu-ray disk, or a memory associated with the processor 712, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 712 and/or embodied in firmware ordedicated hardware. Further, although the example program(s) isdescribed with reference to the flowcharts illustrated in FIGS. 5 and 6,many other methods of implementing the example searcher 114 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined.

As mentioned above, the example processes of FIGS. 5 and 6 may beimplemented using coded instructions (e.g., computer readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage media in whichinformation is stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term tangible computerreadable storage medium is expressly defined to include any type ofcomputer readable storage device and/or storage disc and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIGS. 5 and 6 may be implemented using coded instructions(e.g., computer readable instructions) stored on a non-transitorycomputer readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage media in which informationis stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

FIG. 5 begins with an initiation of the searcher 114 of FIGS. 1 and/or 2(block 500). The initiation of the searcher 114 corresponds to, forexample, the search provider 100 making the searcher 114 available tothe coder 106 of FIG. 1 and/or the search requester 102 of FIG. 1. Asdescribed above, the example searcher 114 generates, maintains andutilized count statistics indicative of the content of the productdatabase 104. In the example of FIG. 5, generations and/or updates ofthe count statistics are implemented by the example statistics generator200 according to a schedule (e.g., once per week) and/or any othersuitable triggering mechanism. When the example statistics generator 200of FIG. 2 is triggered (block 502), the example statistics generator 200counts the number of records that satisfy each characteristic-value pairpresent in the product database 104 at a time corresponding to thetriggering of the statistics generator 200 (block 504). In the exampleof FIG. 5, the count statistics are stored in the example countstatistics database 202 of FIG. 2 (block 506).

The example interface 204 of FIG. 2 determines whether a search requesthas been received (block 508). If not, control passes to block 502 andit is determined whether the statistics generator 200 has beentriggered. If a search request has been received (block 508), theexample interface 204 determines whether the received search request isa single-criterion request or a multiple-criteria request (block 510).If the search request only includes one search criterion, the exampleanalyzer 206 of FIG. 2 searches the product database 104 to identifyrecords satisfying the search request (block 512). Otherwise, if thereceived search request includes more than one search criteria (block510), the example searcher 114 of FIG. 2 develops one or more reducedsets of records based on logical grouping(s) of the search criteria andthe data of the count statistics database 202 and performs one or moresearches using the developed reduced set(s) (block 514). The example ofblock 514 is described in detail below in connection with FIG. 6. Theresults of the operations performed by the example searcher 114 areconveyed to the search requester (block 516). The example of FIG. 5 thenends (block 518).

FIG. 6 begins with the example grouper 208 of FIG. 2 receiving thecriteria of a multiple-criteria search request. The example grouper 208identifies logical relationship(s) between the search criteria andgroups the search criteria accordingly (block 600). In the illustratedexample, grouping the search criteria includes identifying searchcriteria related via an AND operator and assigning the AND-relatedsearch criteria to the same group. The example statistics obtainer 210also receives the search criteria of the search requests and uses thereceived data to obtain corresponding count statistics from the examplecount statistics database 202 (block 602). In some examples, thestatistics generator 200 also generates statistics for any dynamiccharacteristic-value pairs of the received search request.

Using the obtained and/or generated counts, the example drivingcriterion identifier 212 selects one of the criteria of each group asthe driving criterion for the respective group (block 604). In theillustrated example, the driving criterion identifier 212 selects thecriterion satisfying the least amount of records of the product database104 and, thus, having the lowest count in the statistics. The examplereduced set generator 214 uses the selected driving search criterion foreach group to generate a reduced set of records (block 606). In theillustrated example, the reduced set generator 214 queries the productdatabase 104 with the selected driving search criterion to retrieve therecords of the product database 104 satisfying the selected drivingsearch criterion. The results generated by the reduced set generator 214are stored as one or more reduced sets of records (block 608). Asdescribed above, the reduced set(s) of records are searched for thenon-driving criterion to complete the requested search. The ability tosearch the reduced set(s) of records rather than the entire productdatabase 104 for the non-driving criterion or criteria represents asignificant improvement in search speed.

FIG. 7 is a block diagram of an example processor platform 700 capableof executing the instructions of FIGS. 5 and 6 to implement the examplesearcher 114 of FIGS. 1 and/or 2. The processor platform 700 can be, forexample, a personal computer, an Internet appliance, a server, and/orany other type of computing device.

The processor platform 700 of the instant example includes a processor712. For example, the processor 712 can be implemented by one or moremicroprocessors or controllers from any desired family or manufacturer.

The processor 712 includes a local memory 713 (e.g., a cache) and is incommunication with a main memory including a volatile memory 714 and anon-volatile memory 716 via a bus 718. The volatile memory 714 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 716 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 714 and thenon-volatile memory 716 is controlled by a memory controller.

The example processor platform 700 of FIG. 7 also includes an interfacecircuit 720. The interface circuit 720 may be implemented by any type ofinterface standard, such as an Ethernet interface, a universal serialbus (USB), and/or a PCI express interface.

In the example of FIG. 7, one or more input devices 722 are connected tothe interface circuit 720. The input device(s) 722 permit a user toenter data and commands into the processor 712. The input device(s) canbe implemented by, for example, a keyboard, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

In the example of FIG. 7, one or more output devices 724 are alsoconnected to the interface circuit 720. The output devices 724 can beimplemented, for example, by display devices (e.g., a liquid crystaldisplay, a cathode ray tube display (CRT), a printer and/or speakers).The interface circuit 720, thus, typically includes a graphics drivercard.

The interface circuit 720 also includes a communication device such as amodem or network interface card to facilitate exchange of data withexternal computers via a network 726 (e.g., an Ethernet connection, adigital subscriber line (DSL), a telephone line, coaxial cable, acellular telephone system, etc.).

The processor platform 700 also includes one or more mass storagedevices 728 for storing software and data. Examples of such mass storagedevices 728 include floppy disk drives, hard drive disks, compact diskdrives and digital versatile disk (DVD) drives. The mass storage device728 may implement the frame database 318 of FIG. 3.

The coded instructions 732 of FIGS. 5 and/or 6 may be stored in the massstorage device 728, in the volatile memory 714, in the non-volatilememory 716, and/or on a removable storage medium such as a CD or DVD.

Although certain methods, apparatus, and articles of manufacture havebeen described herein, the scope of coverage of this patent is notlimited thereto. To the contrary, this patent covers all methods,apparatus, and articles of manufacture fairly falling within the scopeof the appended claims either literally or under the doctrine ofequivalents.

What is claimed is:
 1. An apparatus comprising: means for obtaining to, in response to a search request including a first criterion and a second criterion, determine a first quantity of records in a database satisfying the first criterion and a second quantity of the records in the database satisfying the second criterion; means for driving criterion selecting to: when the first quantity of the records is less than the second quantity of the records, select the first criterion as a basis for generating a subset of the records of the database; when the second quantity of the records is less than the first quantity of the records, select the second criterion as the basis for the generating of the subset of the records of the database; and means for searching the subset of the records; wherein at least one of the obtaining means, the selecting means, or the searching means are implemented via logic circuitry.
 2. The apparatus of claim 1, wherein the searching means is to search the subset of the records for the second criterion when the driving criterion selecting means selects the first criterion as the basis for the generating of the subset of the records.
 3. The apparatus of claim 1, wherein the searching means is to search the subset of the records for the first criterion when the driving criterion selecting means selects the second criterion as the basis for the generating of the subset of the records.
 4. The apparatus of claim 1, wherein the obtaining means is to determine the first quantity of records in the database satisfying the first criterion and the second quantity of the records in the database satisfying the second criterion based on the first and second quantities from a plurality of statistics generated before receipt of the search request.
 5. The apparatus of claim 4, further including a means for statistic generating to generate the plurality of statistics according to a schedule.
 6. The apparatus of claim 1, further including means for grouping to generate a group of criteria including the first criterion and second criterion based on a logical relationship between the first criterion and the second criterion.
 7. The apparatus of claim 6, wherein the grouping means is to assign the logical relationship between the first criterion and the second criterion if the first criterion is not related to the second criterion via a logical operation.
 8. The apparatus of claim 1, wherein the first criterion and second criterion are characteristic-value pairs.
 9. The apparatus of claim 1, wherein the first criterion are related to the second criterion via a logical operator.
 10. The apparatus of claim 1, wherein the database corresponds to a product database, and the records of the database correspond to a consumer good.
 11. An apparatus comprising: memory; machine readable instructions; and a processor to execute the machine readable instructions to: in response to a search request including a first criterion and a second criterion, determine a first quantity of records in a database satisfying the first criterion and a second quantity of records in the database satisfying the second criterion; when the first quantity is less than the second quantity, select the first criterion as a basis for generating a subset of the records of the database; when the second quantity is less than the first quantity, select the second criterion as the basis for the generating of the subset of the records of the database; and search the subset of the records.
 12. The apparatus of claim 11, wherein the processor is to execute the machine readable instructions to, when the first criterion is selected as the basis for the generating of the subset of the records, search the subset of the records for the second criterion.
 13. The apparatus of claim 11, wherein the processor is to execute the machine readable instructions to, when the second criterion is selected as the basis for the generating of the subset of the records, search the subset of records for the first criterion.
 14. The apparatus of claim 11, wherein the processor is to execute the machine readable instructions to obtain the first and second quantities from a plurality of statistics generated before receipt of the search request.
 15. The apparatus of claim 14, wherein the processor is to execute the machine readable instructions to generate the plurality of statistics according to a schedule.
 16. The apparatus of claim 11, wherein the processor is to execute the machine readable instructions to generate a group of criteria to include the first criterion and the second criterion based on a logical relationship between the first criterion and the second criterion.
 17. The apparatus of claim 16, wherein the processor is to execute the machine readable instructions to assign the logical relationship between the first criterion and the second criterion if the first criterion is not related to the second criterion via a logical operator.
 18. The apparatus of claim 11, wherein the first criterion and the second criterion are characteristic-value pairs.
 19. The apparatus of claim 11, wherein the first criterion is related to the second criterion via a logical operator.
 20. The apparatus of claim 11, wherein the database corresponds to a product database, and the records of the database correspond to a consumer good. 